Image Segmentation
- Pixel Level Segmentation
- DeepLap V3
- Instance level Segmentation (Object Detection쪽에 가까움)
- Mask R-CNN
- SegNet
1. CNNs
1.1 Fully Convolution Network
특징
- End-to-end, Pixel-to-pixel prediction
- Backwards convolution for up-sampling
- Per-pixel multinomial logistic loss
단점
- Fixed size receptive field
- Too simple structure to get detailed features
1.2 Deconvolution Network
특징
- Combining unpooling, deconvolution(with crop), and Relu
- Reconstruction of the detailed structure of an object in finer resolution
- Batch-normalization
단점
- Difficult to learn
- Still lose spatial information
1.3 U-Net
특징
- Do not use unpooling(only up-convolution)
- Skip-connection(with concat)
- Do not have fully connected layer
- Elastic deformation
단점
- Didn’t use batch-norm
- VGG is not the best solution for feature extracting
Medical Data용?
1.4 Deep contextual networks
특징
- Auxiliary connection, classifier
- Ensemble
- Lower memory consumption
단점
- Didn’t use batch-norm
- VGG is not the best solution for feature extracting
1.5 FusionNet
특징
- Skip-connection(with summation)
- Residual block(shortcut connection)
- Elastic deformation
단점 : 메모리
1.6 Pyramid Scene Parsing Net
특징
- Pre-trained FCN with ResNet(1/8 sized feature map)
- Pyramid pooling & 1x1 cone
- Bilinear interpolation
- Avg pooling is better than Max pooling
2. RNNs
2.1 Multi-Dimensional RNNs
특징
- GOD GRAVES!!
- 1D RNNs(Bi-directional RNNs) couldn’t explain images well
- Need to access to the surrounding context in all directions
- N-dimensional data : At least 2^(N) hidden layers
- The input layer is size 3(RGB) or 1(Gray) or patch and the output layer(softmax) is size of classes
3. GANs
https://www.slideshare.net/HyungjooCho2/image-segmentation-hjcho